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Xenarios, I.

Publications and source records attributed to Xenarios, I..

At least 19 recordsLinked to original sources
bioRxiv2025

Evaluation of the Ultima Genomics UG 100™ sequencing platform for clinical services

The steep reduction in the cost of genome sequencing started with the introduction of Solexa Sequencing-By-Synthesis technology in 2006 has plateaued recently due to technical limitations in the use of closed flowcells and to the cost of reagents. The Ultima Genomics UG 100 is the first sequencing machine to lower the cost of human genome sequencing to 80$. However, technical limitations in resolving long homopolymer regions undermine the application of this technology to short variant calling in clinical settings. Here, we evaluate the ability of UG 100 to identify short variants in relation to its suitability for clinical accreditation, by comparing it with the Illumina NovaSeq 6000 Systems platform. We focus specifically on the small variant calling performance in long homopolymer regions, both genome-wide and in relation to a set of medically-relevant genes that are challenging to sequence. Our analysis aims at supporting clinicians in determining whether the UG 100 platform is well-suited for their studies, and to guide clinical sequencing centers in evaluating the adoption of this emerging technology.

bioinformatics↗
bioRxiv2025

A combined computational and experimental approach to successfully predict the behavior of metastatic tumor-associated macrophages in humanbreast cancer

We identified in human breast cancer a scarce population of Tumor-Associated Monocytes (TAMs) endowed with a pro-metastatic activity and associated with reduced distant-metastasis free survival of patients. We developed a novel framework combining computational and experimental methodologies to dampen TAM pro-metastatic activity. Multi-modal experimental data from TAMs exposed in vitro to a series of perturbations were collected to build a Boolean dynamical network of TAMs. This framework successfully identified the biological pathways underlying TAM pro-metastatic activity and predicted potent pharmacological interventions that inhibited the pro-metastatic activity of TAMs isolated from patient tumors. This study showcases the power of integrating computational predictions with experimental validation in identifying new therapeutic avenues that can be extended to other cancer types.

systems biology↗
bioRxiv2024

Integrating knowledge, omics and AI to develop patient-specific virtual avatars

We propose a method for creating personalized regulatory networks, enabling the development of virtual avatars for cancer patients, with patient-derived xenograft (PDX) models as a test case. Starting from a Prior Knowledge Network (PKN) based on the hallmarks of cancer, we constructed gene networks that are contextualized to each sample by integrating sample-specific gene expression data. These networks were optimized using a genetic algorithm to align with individual molecular profiles, focusing on key cancer-related processes. Following network optimization, we employed Graph Convolutional Networks (GCNs) to classify samples based the structures and interactions of their individualized network models and molecular profiles. This personalized approach provides insights into drug responses and helps predict treatment outcomes, offering a path toward more targeted cancer therapies. Author summaryCancer treatment can be more effective when therapies are personalized to each patients unique molecular profile. In this study, we introduce a method to create virtual avatars of cancer patients by personalizing regulatory networks using patient-derived xenograft (PDX) models as a proof of concept. Starting from known cancer hallmarks, we developed individualized gene networks for each sample by leveraging their specific gene expression data. These networks were refined with an optimization process to match the distinct molecular characteristics of each sample. By applying advanced machine learning, specifically Graph Convolutional Networks (GCNs), we classified these personalized models to better understand likely drug responses and predict treatment outcomes. This approach brings us closer to tailoring cancer therapies to individual patients, potentially improving treatment success by targeting key cancer pathways unique to each person.

systems biology↗
bioRxiv2024

Seq2scFv: a toolkit for the comprehensive analysis of display libraries from long-read sequencing platforms

Antibodies have emerged as the leading class of biotherapeutics, yet traditional screening methods face significant time and resource challenges in identifying lead candidates. Integrating highthroughput sequencing with computational approaches marks a pivotal advancement in antibody discovery, expanding the antibody space to explore. In this context, a major breakthrough has been the full-length sequencing of single-chain variable fragments (scFvs) used in in vitro display libraries. However, few tools address the task of annotating the paired heavy and light chain variable domains (VH and VL), which is the primary advantage of full-scFv sequencing. To address this methodological gap, we introduce Seq2scFv, a novel open-source toolkit designed for analyzing in vitro display libraries from long-read sequencing platforms. Seq2scFv facilitates the identification and thorough characterization of V(D)J recombination in both VH and VL regions. In addition to providing annotated scFvs, translated sequences and numbered chains, Seq2scFv enables linker inference and characterization, sequence encoding with unique identifiers and quantification of identical sequences across selection rounds, thereby simplifying enrichment identification. With its versatile and standalone functionality, we anticipate that the implementation of Seq2scFv tools in antibody discovery pipelines will efficiently expedite the full characterization of display libraries and potentially facilitate the identification of high-affinity antibody candidates.

immunology↗
bioRxiv2023

Improving population scale statistical phasing with whole-genome sequencing data

Haplotype estimation, or phasing, has gained significant traction in large-scale projects due to its valuable contributions to population genetics, variant analysis, and the creation of reference panels for imputation and phasing of new samples. To scale with the growing number of samples, haplotype estimation methods designed for population scale rely on highly optimized statistical models to phase genotype data, and usually ignore read-level information. Statistical methods excel in resolving common variants, however, they still struggle at rare variants due to the lack of statistical information. In this study we introduce SAPPHIRE, a new method that leverages whole-genome sequencing data to enhance the precision of haplotype calls produced by statistical phasing. SAPPHIRE achieves this by refining haplotype estimates through the realignment of sequencing reads, particularly targeting low-confidence phase calls. Our findings demonstrate that SAPPHIRE significantly enhances the accuracy of haplotypes obtained from state of the art methods and also provides the subset of phase calls that are validated by sequencing reads. Finally, we show that our method scales to large data sets by its successful application to the extensive 3.6 Petabytes of sequencing data of the last UK Biobank 200,031 sample release.

genomics↗
bioRxiv2023

PAPET: a collection of performant algorithms to identify 5-methyl cytosine from PacBio SequelII data

BackgroundCytosine followed by guanine (CpG) rich genomic regions are known important regulatory sequences among vertebrates. Their cytosines can be subjected to a variety of chemical modifications, one of which being 5-methylcytosine (5mC), usually coined CpG methylation. CpG methylation has been demonstrated to have a deep impact on the nearby regulated genes. The advent of single molecule real time sequencing methods have proven to be powerful enablers in this field of research because of the possibility to detect DNA chemical modification directly from the raw sequencing data. In this perspective, several 5mC detection methods have been proposed. ResultsIn this work we present PAPET - PacBio Prediction of Epigenetic Toolkit - a computational method to detect 5mC directly from PacBio SequelII raw sequencing data. We characterized the 5mC signatures in the raw data and proposed a framework to model them. We also assessed the effect of the DNA sequence alone on the signal and propose a normalization method to leverage this effect. From this, we designed a probabilistic approach to predict the presence of cytosine methylation from the sequencing kinetics and benchmarked it. ConclusionsWe apply this framework to predict CpG methylation from SequelII data and demonstrate that the classifiers compare equally with PacBios prediction method counterparts in terms of AUC and achieve very high specificity and precision.

bioinformatics↗
bioRxiv2023

Reference mouse strain assemblies for BALB/c Nude and NOD/SCID mouse models

Mouse xenograft models play a vital role in tumor studies for research as well as for screening of drugs for the pharmaceutical industry. In particular, models with compromised immunity are favorable to increase the success of transplantation, such as e.g. NOD/SCID and BALB/c Nude strains. The genomic sequence and alterations of many of these models still remain elusive and might hamper a models further optimization or proper adapted usage. This can be in respect to treatments (e.g. NOD/SCID sensitivity to radiation), experiments or analysis of derived sequencing data of such models. Here we present the genome assemblies for the NOD/SCID and BALB/c nude strains to overcome this short-coming for the future and improve our understanding of these models in the process. We highlight as well first insights into observed genomic differences for these models compared to the C57BL/6 reference genome. Genome assemblies for both are close to full chromosome representations and provided with liftover annotations from the GRCm39 reference genome.

genomics↗
bioRxiv2022

Repeat Detector: accurate, efficient, and flexible sizing of expanded CAG/CTG repeats from targeted DNA sequencing

Targeted DNA sequencing approaches will improve how the size of short tandem repeats is measured for diagnostic tests and pre-clinical studies. The expansion of these sequences causes dozens of disorders, with longer tracts generally leading to a more severe disease. Interrupted alleles are sometimes present within repeats and can alter disease manifestation. Determining repeat size mosaicism and identifying interruptions in targeted sequencing datasets remains a major challenge. This is in part because standard alignment tools are ill-suited for repetitive and unstable sequences. To address this, we have developed Repeat Detector (RD), a deterministic profile weighting algorithm for counting repeats in targeted sequencing data. We tested RD using blood-derived DNA samples from Huntingtons disease and Fuchs endothelial corneal dystrophy patients sequenced using either Illumina MiSeq or Pacific Biosciences single-molecule, real-time sequencing platforms. RD was highly accurate in determining repeat sizes of 609 blood-derived samples from Huntingtons disease individuals and did not require prior knowledge of the flanking sequences. Furthermore, RD can be used to identify alleles with interruptions and provide a measure of repeat instability within an individual. RD is therefore highly versatile and may find applications in the diagnosis of expanded repeat disorders and the development of novel therapies.

neuroscience↗
bioRxiv2021

Towards mouse genetic-specific RNA-sequencing read mapping

Genetic variations affect behavior and cause disease but understanding how these variants drive complex traits is still an open question. A common approach is to link the genetic variants to intermediate molecular phenotypes such as the transcriptome using RNA-sequencing (RNA-seq). Paradoxically, these variants between the samples are usually ignored at the beginning of RNA-seq analyses of many model organisms. This can skew the transcriptome estimates that are used later for downstream analyses, such as expression quantitative trait locus (eQTL) detection. Here, we assessed the impact of reference-based analysis on the transcriptome and eQTLs in a widely-used mouse genetic population: the BXD panel of recombinant inbred lines. We highlight existing reference bias in the transcriptome data analysis and propose practical solutions which combine available genetic variants, genotypes, and genome reference sequence. The use of custom BXD line references improved downstream analysis compared to classical genome reference. These insights would likely benefit genetic studies with a transcriptomic component and demonstrate that genome references might need to be reassessed and improved.

bioinformatics↗
bioRxiv2021

A novel anti-influenza combined therapy assessed by single cell RNA-sequencing

Influenza makes millions of people ill every year, placing a large burden on the healthcare system and the economy. To develop a novel treatment against influenza, we combined virucidal sialylated cyclodextrins with interferon lambda and demonstrated, in human airway epithelia, that the two compounds inhibit the replication of a clinical H1N1 strain more efficiently when administered together rather than alone. We investigated the mechanism of action of the combined treatment by single cell RNA sequencing analysis and found that both the single and combined treatments impair viral replication to different extents across distinct epithelial cell types. We also showed that each cell type comprises multiple sub-types, whose proportions are altered by H1N1 infection, and assess the ability of the treatments to restore them. To the best of our knowledge this is the first study investigating the effectiveness of an antiviral therapy by transcriptomic studies at the single cell level.

genomics↗
bioRxiv2019

Integrated Proteogenomic Deep Sequencing and Analytics Accurately Identify Non-Canonical Peptides in Tumor Immunopeptidomes

Efforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides that derive from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass-spectrometry (MS)-based proteogenomics approaches are required to robustly identify these non-canonical peptides. We present an MS-based analytical approach that characterizes the non-canonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single cell transcriptomics, ribosome profiling, and a combination of two MS/MS search tools. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides and of an immunogenic peptide from a downstream reading frame in the melanoma stem cell marker gene ABCB5. It holds great promise for the discovery of novel cancer antigens for cancer immunotherapy.

cancer biology↗
bioRxiv2019

Three-dimensional chromatin interactions remain stable upon CAG/CTG repeat expansion

Expanded CAG/CTG repeats underlie thirteen neurological disorders, including myotonic dystrophy (DM1) and Huntingtons disease (HD). Upon expansion, CAG/CTG repeat loci acquire heterochromatic characteristics. This observation raises the hypothesis that repeat expansion provokes changes to higher order chromatin folding and thereby affects both gene expression in cis and the genetic instability of the repeat tract. Here we tested this hypothesis directly by performing 4C sequencing at the DMPK and HTT loci from DM1 and HD patient-derived cells. Surprisingly, chromatin contacts remain unchanged upon repeat expansion at both loci. This was true for loci with different DNA methylation levels and CTCF binding. Repeat sizes ranging from 15 to 1,700 displayed strikingly similar chromatin interaction profiles. Our findings argue that extensive changes in heterochromatic properties are not enough to alter chromatin folding at expanded CAG/CTG repeat loci. Moreover, the ectopic insertion of an expanded repeat tract did not change three-dimensional chromatin contacts. We conclude that expanded CAG/CTG repeats have little to no effect on chromatin conformation.

neuroscience↗
bioRxiv2019

Enzyme annotation in UniProtKB using Rhea

MotivationTo provide high quality computationally tractable enzyme annotation in UniProtKB using Rhea, a comprehensive expert-curated knowledgebase of biochemical reactions which describes reaction participants using the ontology ChEBI (Chemical Entities of Biological Interest).\n\nResultsWe replaced existing textual descriptions of biochemical reactions in UniProtKB with their equivalents from Rhea, which is now the standard for annotation of enzymatic reactions in UniProtKB. We developed improved search and query facilities for the UniProt website, REST API, and SPARQL endpoint that leverage the chemical structure data, nomenclature, and classification that Rhea and ChEBI provide.\n\nAvailability and ImplementationUniProtKB at https://www.uniprot.org/; UniProt REST API at https://www.uniprot.org/help/api; UniProt SPARQL endpoint at https://sparql.uniprot.org/sparql; Rhea at https://www.rhea-db.org/.\n\nContactanne.morgat@sib.swiss

bioinformatics↗
bioRxiv2019

Simple and complex interactions between sleep-wake driven and circadian processes shape daily genome regulatory dynamics in the mouse

The timing and duration of sleep results from the interaction between a sleep-wake driven, or homeostatic, process (S) and a circadian process (C), and involves changes in gene expression and genomic regulation. Unraveling the respective contributions of S and C, and their interaction, to transcriptional and epigenomic regulatory dynamics requires sampling over time under unperturbed conditions and conditions of perturbed sleep. Here, we profiled mRNA expression and chromatin accessibility in the cerebral cortex of mice over a three-day period, including a 6-hour sleep deprivation (SD) on day two. Mathematical modeling established that a large proportion of rhythmic genes are actually governed by Process S with varying degrees of interaction with Process C, sometimes working in opposition. Remarkably, SD causes long-term effects on gene expression dynamics, outlasting phenotypic recovery, most strikingly illustrated by a dampening of the oscillation of most core clock genes, including Bmal1, suggesting that enforced wakefulness directly impacts the molecular clock machinery. Chromatin accessibility proved highly plastic and dynamically affected by SD. Distal regions, rather than promoters, display dynamics corresponding to gene transcription, implying that changes in mRNA expression result from constantly accessible promoters under the influence of distal enhancers or repressors. Srf was predicted as a transcriptional regulator driving immediate response, suggesting that Srf activity mirrors the build-up and release of sleep pressure. Our results demonstrate that a single, short SD has long-term aftereffects at the genomic regulatory level. Such effects might accumulate with repeated sleep restrictions, thereby contributing to their adverse health effects.\n\nSignificance statementWhen and how long we sleep is determined by the time-of-day and how long we have been awake, which are tracked molecularly by a circadian and a sleep-wake driven process, respectively. We measured the long-term consequences of a short-term sleep deprivation (SD) on gene expression and regulation in the mouse brain, and used mathematical models to determine the relative contributions of the circadian and sleep-wake driven processes. We find that many genes, including most of the genes that constitute the molecular circadian clock, are perturbed by SD long after the mice ceased showing behavioral signs of sleep loss. Our results have implications for human health, given the high prevalence of insufficient and poor quality sleep in our contemporary society.

genomics↗
bioRxiv2019

HAMAP rules as SPARQL - A portable annotation pipeline for genomes and proteomes

MotivationGenome and proteome annotation pipelines are generally custom built and therefore not easily reusable by other groups, which leads to duplication of effort, increased costs, and suboptimal results. One cost-effective way to increase the data quality in public databases is to encourage the adoption of annotation standards and technological solutions that enable the sharing of biological knowledge and tools for genome and proteome annotation.\n\nResultsWe have translated the rules of our HAMAP proteome annotation pipeline to queries in the W3C standard SPARQL 1.1 syntax and applied them with two off-the-shelf SPARQL engines to UniProtKB/Swiss-Prot protein sequences described in RDF format. This approach is applicable to any genome or proteome annotation pipeline and greatly simplifies their reuse.\n\nAvailabilityHAMAP SPARQL rules and documentation are freely available for download from the HAMAP FTP site ftp://ftp.expasy.org/databases/hamap/hamapsparql.tar.gz under a CC-BY-ND 4.0 license. The annotations generated by the rules are under the CC-BY 4.0 license.\n\nContacthamap@sib.swiss\n\nSupplementary informationSupplementary data are included at the end of this document.

bioinformatics↗
bioRxiv2019

PamgeneAnalyzeR open and reproducible pipeline for kinase profiling

Protein phosphorylation - catalyzed by protein kinases - is the most common post-translational modification. It increases the functional diversity of the proteome and influences various aspects of normal physiology and can be altered in disease states. High throughput profiling of kinases is becoming an essential experimental approach to investigate their activity and this can be achieved using technologies such as PamChip(R) arrays provided by PamGene for kinase activity measurement. Here we present \"pamgeneAnalyzeR\", an R package developed as an alternative to the manual steps necessary to extract the data from PamChip(R) peptide microarrays images in a reproducible and robust manner. The extracted data can be directly used for downstream analysis.\n\nAvailability and implementationPamgeneAnalyzeR is implemented in R and can be obtained from https://github.com/amelbek/pamgeneAnalyzeR.

bioinformatics↗
bioRxiv2019

A multi-omics digital research object for the genetics of sleep regulation.

More and more researchers make use of multi-omics approaches to tackle complex cellular and organismal systems. It has become apparent that the potential for re-use and integrate data generated by different labs can enhance knowledge. However, a meaningful and efficient re-use of data generated by others is difficult to achieve without in depth understanding of how these datasets were assembled. We therefore designed and describe in detail a digital research object embedding data, documentation and analytics on mouse sleep regulation. The aim of this study was to bring together electrophysiological recordings, sleep-wake behavior, metabolomics, genetics, and gene regulatory data in a systems genetics model to investigate sleep regulation in the BXD panel of recombinant inbred lines. We here showcase both the advantages and limitations of providing such multi-modal data and analytics. The reproducibility of the results was tested by a bioinformatician not implicated in the original project and the robustness of results was assessed by re-annotating genetic and transcriptome data from the mm9 to the mm10 mouse genome assembly.

bioinformatics↗
bioRxiv2018

Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome

Spliced peptides are short protein fragments spliced together in the proteasome by peptide bond formation. True estimation of the contribution of proteasome-spliced peptides (PSPs) to the global Human Leukocyte Antigen (HLA) ligandome is critical. A recent study suggested that PSPs contribute up to 30% of the HLA ligandome. We performed a thorough reanalysis of the reported results using multiple computational tools and various validation steps and concluded that only a fraction of the proposed PSPs passes the quality filters. To better estimate the actual number of PSPs, we present an alternative workflow. We performed de-novo sequencing of the HLA-peptide spectra and discarded all de-novo sequences found in the UniProt database. We checked whether the remaining de-novo sequences could match spliced peptides from human proteins. The spliced sequences were appended to the UniProt fasta file, which was searched by two search tools at a FDR of 1%. We find that maximally 2-4% of the HLA ligandome could be explained as spliced protein fragments. The majority of these potential PSPs have good peptide-spectrum match properties and are predicted to bind the respective HLA molecules. However, it remains to be shown how many of these potential PSPs actually originate from proteasomal splicing events.

bioinformatics↗